Video matching with a messaging application

ABSTRACT

Aspects of the present disclosure involve a system and a method for performing operations comprising: identifying a plurality of features for frames of a video received by a messaging application server; assigning a first sequence of a first subset of the plurality of features and a second sequence of a second subset of the plurality of features respectively to a first nearest visual codebook cluster and a second nearest visual codebook cluster; applying the first and second nearest visual codebook clusters to a visual search database to identify a plurality of candidate matching videos; selecting a given matching video from the plurality of candidate matching videos based on a rank representing similarity of the given matching video to the video received by the messaging application server; and accessing an augmented reality experience corresponding to the given matching video.

CLAIM OF PRIORITY

This application claims the benefit of priority to U.S. ProvisionalApplication Ser. No. 63/198,146, filed on Sep. 30, 2020, which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to detecting matching videoframes using a messaging application.

BACKGROUND

Modern day user devices provide messaging applications that allow usersto exchange messages with one another. Such messaging applications mayincorporate graphics in such communications. Users can select betweenvarious predetermined graphics to incorporate into their communications.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some nonlimiting examples areillustrated in the figures of the accompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging system, inaccordance with some examples, that has both client-side and server-sidefunctionality.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples.

FIG. 5 is a diagrammatic representation of a visual search module, inaccordance with some examples.

FIG. 6 is a diagrammatic representation of visual codebook clusters, inaccordance with some examples.

FIG. 7 is a diagrammatic representation of a visual bag of wordshistogram, in accordance with some examples.

FIG. 8 is a diagrammatic representation of descriptor correspondencesbetween a query video and a marker video, in accordance with someexamples.

FIGS. 9A-E are diagrammatic representations of graphical userinterfaces, in accordance with some examples.

FIG. 10 is a flowchart illustrating example operations of the messagingapplication server, according to example embodiments.

FIG. 11 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 12 is a block diagram showing a software architecture within whichexamples may be implemented.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments. It will be evident, however, to those skilled in the art,that embodiments may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Typically, users exchange messages with each other using messagingapplications. Such applications allow users to select from a predefinedlist of images to send to one another. Users are increasinglycommunicating with each other using such images to convey theirthoughts. However, finding the right images to convey a particularthought can be tedious and time consuming. Specifically, the user has tomanually search using keywords for a particular image that conveys agiven message. This requires navigating through multiple pages ofinformation until the desired image is found. Given the complexity andamount of time it takes to find the right image, users becomediscouraged from communicating using the images, which results in awaste of resources or lack of use.

Certain systems allow a user to specify a type of object that is presentin an image that is captured. These systems then search the capturedimage for the specific type of object that is specified. If the objectis found, then a corresponding augmented reality experience is providedto the user. These systems burden the user with having to search throughmany types of objects to find the one of interest before the system cananalyze the image to determine whether the image contains the specifiedobject. This manual process and lack of automation discourages use ofthese functions which also wastes resources.

The disclosed embodiments improve the efficiency of using the electronicdevice by providing a system that automatically and intelligentlyselects and presents augmented reality experiences for a user to use toaugment a captured image to be shared with another user in a messagingapplication based on a sequence of features of a video captured by theuser. Specifically, the disclosed embodiments activate one or moreaugmented reality experiences by scanning a video that is captured by aclient device without having the user specify the type of video or typeof objects that are present in the video.

According to the disclosed embodiments, a messaging application serverimplemented by one or more processors identifies a plurality of featuresfor frames of a video received from a client device. The messagingapplication server assigns a first sequence of a first subset of theplurality of features and a second sequence of a second subset of theplurality of features respectively to a first nearest visual codebookcluster and a second nearest visual codebook cluster. The messagingapplication server applies the first and second nearest visual codebookclusters to a visual search database to identify a plurality ofcandidate matching videos. The messaging application server selects agiven matching video from the plurality of candidate matching videosbased on a rank representing similarity of the given matching video tothe video received by the messaging application server and accesses anaugmented reality experience corresponding to the given matching video.

In this way, the disclosed embodiments improve the efficiency of usingthe electronic device by reducing the number of screens and interfaces auser has to navigate through to find an augmented reality item or ARexperience to augment an image captured by the user to then share withother users. This is done by determining attributes of objects depictedacross frames in a video captured by the client or user device with themessaging application and then searching for augmented reality items orAR experiences that are associated with the attributes or sequence ofattributes for presentation to the user. This reduces the deviceresources (e.g., processor cycles, memory, and power usage) needed toaccomplish a task with the device.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104 and other external applications 109 (e.g., third-partyapplications). Each messaging client 104 is communicatively coupled toother instances of the messaging client 104 (e.g., hosted on respectiveother client devices 102), a messaging server system 108 and externalapp(s) servers 110 via a network 112 (e.g., the Internet). A messagingclient 104 can also communicate with locally-hosted third-partyapplications 109 using Applications Program Interfaces (APIs).

A messaging client 104 is able to communicate and exchange data withother messaging clients 104 and with the messaging server system 108 viathe network 112. The data exchanged between messaging clients 104, andbetween a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 112 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces (s) of the messaging client 104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 116 is coupled to, andprovides a programmatic interface to, application servers 114. Theapplication servers 114 are communicatively coupled to a database server120, which facilitates access to a database 126 that stores dataassociated with messages processed by the application servers 114.Similarly, a web server 128 is coupled to the application servers 114,and provides web-based interfaces to the application servers 114. Tothis end, the web server 128 processes incoming network requests overthe Hypertext Transfer Protocol (HTTP) and several other relatedprotocols.

The Application Program Interface (API) server 116 receives andtransmits message data (e.g., commands and message payloads) between theclient device 102 and the application servers 114. Specifically, theApplication Program Interface (API) server 116 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 114. The Application Program Interface (API) server116 exposes various functions supported by the application servers 114,including account registration, login functionality, the sending ofmessages, via the application servers 114, from a particular messagingclient 104 to another messaging client 104, the sending of media filesimages or video) from a messaging client 104 to a messaging server 118,and for possible access by another messaging client 104, the settings ofa collection of media data (e.g., story), the retrieval of a list offriends of a user of a client device 102, the retrieval of suchcollections, the retrieval of messages and content, the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph), the location of friends within a social graph, and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 114 host a number of server applications andsubsystems, including for example a messaging server 118, an imageprocessing server 122, and a social network server 124. The messagingserver 118 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor- and memory-intensive processingof data may also be performed server-side by the messaging server 118,in view of the hardware requirements for such processing.

The application servers 114 also include an image processing server 122that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 118. Detailedfunctionality of the image processing server 122 is shown and describedin connection with FIG. 5 .

Image processing server 122 is used to implement scan functionality ofthe augmentation system 208. Scan functionality includes activating andproviding one or more augmented reality experiences on a client device102 when an image is captured by the client device 102. Specifically,the messaging application 104 on the client device 102 can be used toactivate a camera. The camera displays one or more real-time images to auser along with one or more options. The user can select a scan optionor can press and hold the user's finger on the image currently displayedby the client device 102. In response, the augmentation system 208 incommunication with the image processing server 122 analyzes the imagecurrently displayed and identifies one or more augmented realityexperiences associated with an object depicted in the image.

In one embodiment, the image processing server 122 includes a front-endmodule that acts as a router to different micro-services/containerswhich communicate with each other through various proxies. The front-endmodule obtains metadata associated with the captured image, such as thelocation, time, known features, known objects, orientation, size, and soforth. The front-end module uses the metadata to route the image forprocessing by specific analyzer modules. Analyzer modules include anycombination of visual matching video analyzer (e.g., visual searchmodule discussed in connection with FIG. 5 ), a marker search analyzer,a currency analyzer, a museum artwork analyzer, a brute force analyzer,a logo detection analyzer, an open images analyzer, a face detection, anot safe for work analyzer, a content finder analyzer, a lookalikeanalyzer, and various other deep learning based analyzers. The analyzerscan work in parallel or sequentially or may be selectively activated toprocess a given image by the front-end module. Each analyzer moduleperforms visual matching for a specific use case against a providedimage. In some cases, the visual search matching image analyzer can betrained and used to perform the function of all or a given subset of theanalyzer modules.

Each analyzer module performs computer vision or machine learningprocesses and returns annotations relevant to the content contained inthe given image or video. Such annotations can include identifiers ofspecific augmented reality experiences, logo names, bounding coordinatesfor objects and brands, metadata about a matching painting, and soforth. Using the annotations, the image processing server 122communicates with the augmentation system 208 to select and activate aparticular augmented reality experience, such as the presentation of oneor more augmented reality elements on a real-time or stored video orimage displayed on the client device 102. Specifically, the front-endmodule processes the annotations returned by one or more of the analyzermodules, ranks and filters the annotations to select a subset ofannotations. The selected subset of annotations or the highest rankedannotation is provided to the augmentation system 208 to select andactivate a corresponding augmented reality experience associated withthe annotation.

In some embodiments, the visual search module of the image processingserver 122 receives a query video (including a certain quantity ofconsecutive frames, such as a number of frames corresponding to 3-5seconds of video) and performs pre-processing operations on the queryvideo. For example, the visual search module transforms raw image bytesor information into image data (e.g., Numpy array or tensors) and canperform re-scaling and normalization. In some implementations, thepre-processing operations include resizing and reorienting an inputvideo to match a template size and orientation. The visual search moduleprocesses the video with a feature extractor to transform the image dataof each frame of the video (or subset of frames of the video) into afeature map representing the query video. The features extracted by thefeature extractor are provided to a candidate retriever which accesses avisual search database to retrieve a set of candidate matching videos.For example, the candidate retriever processes the video with a visualcodebook and term-frequency inverse document frequency (TF-IDF) matrixof image descriptors derived from video features to identify the set ofcandidate matching videos. The candidate matching videos are filteredbased on a specified criteria. (e.g., based on geometric attributes ofthe matching videos). The filtered videos are provided to a responsecreator to generate a list of retrieval results or annotations which areused to select and activate a given one or more augmented realityexperience.

In some implementations, the visual search module can process videos togenerate annotations or find matching videos using various computervision techniques that process various feature sets at specific keypoints or regions in a video or deep learning object detection models.The visual search module is trained offline to index data in a way thatcan be surfaced quickly and efficiently by the image processing server122. In one embodiment, the visual search module is trained to generatea visual bag of words and use the visual bag of words to detect presenceof an object or multiple objects in a given query video. The trainingand use process for the visual search module is shown and discussedbelow in connection with FIG. 5 .

The social network server 124 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 118. To this end, the social network server 124maintains and accesses an entity graph 308 (as shown in FIG. 3 ) withinthe database 126. Examples of functions and services supported by thesocial network server 124 include the identification of other users ofthe messaging system 100 with which a particular user has relationshipsor is “following,” and also the identification of other entities andinterests of a particular user.

Returning to the messaging client 104, features and functions of anexternal resource (e.g., a third-party application 109 or applet) aremade available to a user via an interface of the messaging client 104.The messaging client 104 receives a user selection of an option tolaunch or access features of an external resource (e.g., a third-partyresource), such as external apps 109. The external resource may be athird-party application (external apps 109) installed on the clientdevice 102 (e.g., a “native app”), or a small-scale version of thethird-party application (e.g.; an “applet”) that is hosted on the clientdevice 102 or remote of the client device 102 (e.g., on third-partyservers 110). The small-scale version of the third-party applicationincludes a subset of features and functions of the third-partyapplication (e.g., the full-scale, native version of the third-partystandalone application) and is implemented using a markup-languagedocument. In one example, the small-scale version of the third-partyapplication (e.g., an “applet”) is a web-based, markup-language versionof the third-party application and is embedded in the messaging client104. In addition to using markup-language documents (e.g., a .*ml file),an applet may incorporate a scripting language (e.g., a .*js file or a.json file) and a style sheet (e.g., a .*ss file).

In response to receiving a user selection of the option to launch oraccess features of the external resource (external app 109), themessaging client 104 determines whether the selected external resourceis a web-based external resource or a locally-installed externalapplication. In some cases, external applications 109 that are locallyinstalled on the client device 102 can be launched independently of andseparately from the messaging client 104, such as by selecting an icon,corresponding to the external application 109, on a home screen of theclient device 102. Small-scale versions of such external applicationscan be launched or accessed via the messaging client 104 and, in someexamples, no or limited portions of the small-scale external applicationcan be accessed outside of the messaging client 104. The small-scaleexternal application can be launched by the messaging client 104receiving, from a external app(s) server 110, a markup-language documentassociated with the small-scale external application and processing sucha document.

In response to determining that the external resource is alocally-installed external application 109, the messaging client 104instructs the client device 102 to launch the external application 109by executing locally-stored code corresponding to the externalapplication 109. In response to determining that the external resourceis a web-based resource, the messaging client 104 communicates with theexternal app(s) servers 110 to obtain a markup-language documentcorresponding to the selected resource. The messaging client 104 thenprocesses the obtained markup-language document to present the web-basedexternal resource within a user interface of the messaging client 104.

The messaging client 104 can notify a user of the client device 102, orother users related to such a user (e.g., “friends”), of activity takingplace in one or more external resources. For example, the messagingclient 104 can provide participants in a conversation (e.g., a chatsession) in the messaging client 104 with notifications relating to thecurrent or recent use of an external resource by one or more members ofa group of users. One or more users can be invited to join in an activeexternal resource or to launch a recently-used but currently inactive(in the group of friends) external resource. The external resource canprovide participants in a conversation, each using a respectivemessaging client messaging clients 104, with the ability to share anitem, status, state, or location in an external resource with one ormore members of a group of users into a chat session. The shared itemmay be an interactive chat card with which members of the chat caninteract, for example, to launch the corresponding external resource,view specific information within the external resource, or take themember of the chat to a specific location or state within the externalresource. Within a given external resource, response messages can besent to users on the messaging client 104. The external resource canselectively include different media items in the responses, based on acurrent context of the external resource.

The messaging client 104 can present a list of the available externalresources (e.g., third-party or external applications 109 or applets) toa user to launch or access a given external resource. This list can bepresented in a context-sensitive menu. For example, the iconsrepresenting different ones of the external application 109 (or applets)can vary based on how the menu is launched by the user (e.g., from aconversation interface or from a non-conversation interface).

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 114. The messaging system 100 embodies a numberof subsystems, which are supported on the client side by the messagingclient 104 and on the sever side by the application servers 114. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, an augmentation system 208, a mapsystem 210, a game system 212, and an external resource system 220.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 118. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 furthermore includes a curationinterface 206 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface206 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 208 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system208 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 208 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 208 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text, a graphical element, or image that canbe overlaid on top of a photograph taken by the client device 102. Inanother example, the media overlay includes an identification of alocation overlay (e.g., Venice beach), a name of a live event, or a nameof a merchant overlay (e.g., Beach Coffee House). In another example,the augmentation system 208 uses the geolocation of the client device102 to identify a media overlay that includes the name of a merchant atthe geolocation of the client device 102. The media overlay may includeother indicia associated with the merchant. The media overlays may bestored in the database 126 and accessed through the database server 120.

In some examples, the augmentation system 208 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 208 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

In other examples, the augmentation system 208 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the augmentation system 208 associates the media overlay of thehighest bidding merchant with a corresponding geolocation for apredefined amount of time. The augmentation system 208 communicates withthe image processing server 122 to automatically select and activate anaugmented reality experience related to an image captured by the clientdevice 102. Once the augmented reality experience is selected as theuser scans images using a camera in the user's environment, one or moreimages, videos, or augmented reality graphical elements are retrievedand presented as an overlay on top of the scanned images. In some cases,the camera is switched to a front-facing view (e.g., the front-facingcamera of the client device 102 is activated in response to activationof a particular augmented reality experience) and the images from thefront-facing camera of the client device 102 start being displayed onthe client device 102 instead of the rear-facing camera of the clientdevice 102. The one or more images, videos, or augmented realitygraphical elements are retrieved and presented as an overlay on top ofthe images that are captured and displayed by the front-facing camera ofthe client device 102.

The map system 210 provides various geographic location functions, andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 210 enables thedisplay of user icons or avatars (e.g., stored in profile data 316) on amap to indicate a current or past location of “friends” of a user, aswell as media content (e.g., collections of messages includingphotographs and videos) generated by such friends, within the context ofa map. For example, a message posted by a user to the messaging system100 from a specific geographic location may be displayed within thecontext of a map at that particular location to “friends” of a specificuser on a map interface of the messaging client 104. A user canfurthermore share his or her location and status information (e.g.,using an appropriate status avatar) with other users of the messagingsystem 100 via the messaging client 104, with this location and statusinformation being similarly displayed within the context of a mapinterface of the messaging client 104 to selected users.

The game system 212 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games (e.g., web-based games orweb-based applications) that can be launched by a user within thecontext of the messaging client 104, and played with other users of themessaging system 100. The messaging system 100 further enables aparticular user to invite other users to participate in the play of aspecific game, by issuing invitations to such other users from themessaging client 104. The messaging client 104 also supports both voiceand text messaging (e.g., chats) within the context of gameplay,provides a leaderboard for the games, and also supports the provision ofin-game rewards (e.g., coins and items).

The external resource system 220 provides an interface for the messagingclient 104 to communicate with external app(s) servers 110 to launch oraccess external resources. Each external resource (apps) server 110hosts, for example, a markup language (e.g., HTML5) based application orsmall-scale version of an external application (e.g., game, utility,payment, or ride-sharing application that is external to the messagingclient 104). The messaging client 104 may launch a web-based resource(e.g., application) by accessing the HTML5 file from the externalresource (apps) servers 110 associated with the web-based resource. Incertain examples, applications hosted by external resource servers 110are programmed in JavaScript leveraging a Software Development Kit (SDK)provided by the messaging server 118. The SDK includes ApplicationProgramming Interfaces (APIs) with functions that can be called orinvoked by the web-based application. In certain examples, the messagingserver 118 includes a JavaScript library that provides a giventhird-party resource access to certain user data of the messaging client104. HTML5 is used as an example technology for programming games, butapplications and resources programmed based on other technologies can beused.

In order to integrate the functions of the SDK into the web-basedresource, the SDK is downloaded by an external resource (apps) server110 from the messaging server 118 or is otherwise received by theexternal resource (apps) server 110. Once downloaded or received, theSDK is included as part of the application code of a web-based externalresource. The code of the web-based resource can then call or invokecertain functions of the SDK to integrate features of the messagingclient 104 into the web-based resource.

The SDK stored on the messaging server 118 effectively provides thebridge between an external resource (e.g., third-party or externalapplications 109 or applets and the messaging client 104). This providesthe user with a seamless experience of communicating with other users onthe messaging client 104, while also preserving the look and feel of themessaging client 104. To bridge communications between an externalresource and a messaging client 104, in certain examples, the SDKfacilitates communication between external resource servers 110 and themessaging client 104. In certain examples, a WebViewJavaScriptBridgerunning on a client device 102 establishes two one-way communicationchannels between a external resource and the messaging client 104.Messages are sent between the external resource and the messaging client104 via these communication channels asynchronously. Each SDK functioninvocation is sent as a message and callback. Each SDK function isimplemented by constructing a unique callback identifier and sending amessage with that callback identifier.

By using the SDK, not all information from the messaging client 104 isshared with external resource servers 110. The SDK limits whichinformation is shared based on the needs of the external resource. Incertain examples, each external resource server 110 provides an HTML5file corresponding to the web-based external resource to the messagingserver 118. The messaging server 118 can add a visual representation(such as a box art or other graphic) of the web-based external resourcein the messaging client 104. Once the user selects the visualrepresentation or instructs the messaging client 104 through a GUI ofthe messaging client 104 to access features of the web-based externalresource, the messaging client 104 obtains the HTML5 file andinstantiates the resources necessary to access the features of theweb-based external resource.

The messaging client 104 presents a graphical user interface (e.g., alanding page or title screen) for an external resource. During, before,or after presenting the landing page or title screen, the messagingclient 104 determines whether the launched external resource has beenpreviously authorized to access user data of the messaging client 104.In response to determining that the launched external resource has beenpreviously authorized to access user data of the messaging client 104,the messaging client 104 presents another graphical user interface ofthe external resource that includes functions and features of theexternal resource. In response to determining that the launched externalresource has not been previously authorized to access user data of themessaging client 104, after a threshold period of time (e.g., 3 seconds)of displaying the landing page or title screen of the external resource,the messaging client 104 slides up (e.g., animates a menu as surfacingfrom a bottom of the screen to a middle of or other portion of thescreen) a menu for authorizing the external resource to access the userdata. The menu identifies the type of user data that the externalresource will be authorized to use. In response to receiving a userselection of an accept option, the messaging client 104 adds theexternal resource to a list of authorized external resources and allowsthe external resource to access user data from the messaging client 104.In some examples, the external resource is authorized by the messagingclient 104 to access the user data in accordance with an OAuth 2framework.

The messaging client 104 controls the type of user data that is sharedwith external resources based on the type of external resource beingauthorized. For example, external resources that include full-scaleexternal applications (e.g., a third-party or external application 109)are provided with access to a first type of user data (e.g., onlytwo-dimensional avatars of users with or without different avatarcharacteristics). As another example, external resources that includesmall-scale versions of external applications (e.g., web-based versionsof third-party applications) are provided with access to a second typeof user data (e.g., payment information, two-dimensional avatars ofusers, three-dimensional avatars of users, and avatars with variousavatar characteristics). Avatar characteristics include different waysto customize a look and feel of an avatar, such as different poses,facial features, clothing, and so forth.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 126 of the messaging server system 108,according to certain examples. While the content of the database 126 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 126 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload. Further details regarding information that may be included in amessage, and included within the message data stored in the messagetable 302, is described below with reference to FIG. 4 .

An entity table 306 stores entity data, and is linked (e.g.,referentially) to an entity graph 308 and profile data 316. Entities forwhich records are maintained within the entity table 306 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 308 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization)interested-based or activity-based, merely for example.

The profile data 316 stores multiple types of profile data about aparticular entity. The profile data 316 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 316 includes, for example, a user name,telephone number, address, settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100, andon map interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

Where the entity is a group, the profile data 316 for the group maysimilarly include one or more avatar representations associated with thegroup, in addition to the group name, members, and various settings(e.g., notifications) for the relevant group.

The database 126 also stores augmentation data, such as overlays orfilters, in an augmentation table 310. The augmentation data isassociated with and applied to videos (for which data is stored in avideo table 304) and images (for which data is stored in an image table312).

Filters, in one example, are overlays that are displayed as overlaid onan image or video during presentation to a recipient user. Filters maybe of various types, including user-selected filters from a set offilters presented to a sending user by the messaging client 104 when thesending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 102.

Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client 104, based on otherinputs or information gathered by the client device 102 during themessage creation process. Examples of data filters include currenttemperature at a specific location, a current speed at which a sendinguser is traveling, battery life for a client device 102, or the currenttime.

Other augmentation data that may be stored within the image table 312includes augmented reality content items (e.g., corresponding toapplying Lenses or augmented reality experiences). An augmented realitycontent item may be a real-time special effect and sound that may beadded to an image or a video. Each augmented reality experience may beassociated with one or more marker videos. In some embodiments, when amarker video is determined to match a query video received from theclient device 102, the corresponding augmented reality experience (e.g.,the augmentation data) of the maker video is retrieved from the imagetable 312 and provided to the client device 102. Various types ofaugmented reality experiences are shown and discussed in connection withFIGS. 9A-E.

As described above, augmentation data includes augmented reality contentitems, overlays, image transformations, AR images, and similar termsthat refer to modifications that may be applied to image data (e.g.,videos or images). This includes real-time modifications, which modifyan image as it is captured using device sensors (e.g., one or multiplecameras) of a client device 102 and then displayed on a screen of theclient device 102 with the modifications. This also includesmodifications to stored content, such as video clips in a gallery thatmay be modified. For example, in a client device 102 with access tomultiple augmented reality content items, a user can use a single videoclip with multiple augmented reality content items to see how thedifferent augmented reality content items will modify the stored clip.For example, multiple augmented reality content items that applydifferent pseudorandom movement models can be applied to the samecontent by selecting different augmented reality content items for thecontent. Similarly, real-time video capture may be used with anillustrated modification to show how video images currently beingcaptured by sensors of a client device 102 would modify the captureddata. Such data may simply be displayed on the screen and not stored inmemory, or the content captured by the device sensors may be recordedand stored in memory with or without the modifications (or both). Insome systems, a preview feature can show how different augmented realitycontent items will look within different windows in a display at thesame time. This can, for example, enable multiple windows with differentpseudorandom animations to be viewed on a display at the same time.

Data and various systems using augmented reality content items or othersuch transform systems to modify content using this data can thusinvolve detection of objects (e.g., faces, hands, bodies, cats, dogs,surfaces, objects, etc.), tracking of such objects as they leave, enter,and move around the field of view in video frames, and the modificationor transformation of such objects as they are tracked. In variousexamples, different methods for achieving such transformations may beused. Some examples may involve generating a three-dimensional meshmodel of the object or objects, and using transformations and animatedtextures of the model within the video to achieve the transformation. Inother examples, tracking of points on an object may be used to place animage or texture (which may be two dimensional or three dimensional) atthe tracked position. In still further examples, neural network analysisof video frames may be used to place images, models, or textures incontent (e.g., images or frames of video). Augmented reality contentitems thus refer both to the images, models, and textures used to createtransformations in content, as well as to additional modeling andanalysis information needed to achieve such transformations with objectdetection, tracking, and placement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device, or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human's face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device, and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of object's elements, characteristic points for each element of anobject are calculated (e.g., using an Active Shape Model (ASM) or otherknown methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshis used in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A first set of first pointsis generated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh, and thenvarious areas based on the points are generated. The elements of theobject are then tracked by aligning the area for each element with aposition for each of the at least one element, and properties of theareas can be modified based on the request for modification, thustransforming the frames of the video stream. Depending on the specificrequest for modification, properties of the mentioned areas can betransformed in different ways. Such modifications may involve changingcolor of areas; removing at least some part of areas from the frames ofthe video stream; including one or more new objects into areas which arebased on a request for modification; and modifying or distorting theelements of an area or object. In various examples, any combination ofsuch modifications or other similar modifications may be used. Forcertain models to be animated, some characteristic points can beselected as control points to be used in determining the entirestate-space of options for the model animation.

In some examples of a computer animation model to transform image datausing face detection, the face is detected on an image with use of aspecific face detection algorithm (e.g., Viola-Jones). Then, an ActiveShape Model (ASM) algorithm is applied to the face region of an image todetect facial feature reference points.

Other methods and algorithms suitable for face detection can be used.For example; in some examples, features are located using a landmark,which represents a distinguishable point present in most of the imagesunder consideration. For facial landmarks, for example, the location ofthe left eye pupil may be used. If an initial landmark is notidentifiable (e.g., if a person has an eyepatch), secondary landmarksmay be used. Such landmark identification procedures may be used for anysuch objects. In some examples, a set of landmarks forms a shape. Shapescan be represented as vectors using the coordinates of the points in theshape. One shape is aligned to another with a similarity transform(allowing translation, scaling, and rotation) that minimizes the averageEuclidean distance between shape points. The mean shape is the mean ofthe aligned training shapes.

In some examples, a search for landmarks from the mean shape aligned tothe position and size of the face determined by a global face detectoris started. Such a search then repeats the steps of suggesting atentative shape by adjusting the locations of shape points by templatematching of the image texture around each point and then conforming thetentative shape to a global shape model until convergence occurs. Insome systems, individual template matches are unreliable, and the shapemodel pools the results of the weak template matches to form a strongeroverall classifier. The entire search is repeated at each level in animage pyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a clientdevice (e.g., the client device 102) and perform complex imagemanipulations locally on the client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, and any othersuitable image or video manipulation implemented by a convolutionalneural network that has been configured to execute efficiently on theclient device 102.

In some examples, a computer animation model to transform image data canbe used by a system where a user may capture an image or video stream ofthe user (e.g., a selfie) using a client device 102 having a neuralnetwork operating as part of a messaging client 104 operating on theclient device 102. The transformation system operating within themessaging client 104 determines the presence of a face within the imageor video stream and provides modification icons associated with acomputer animation model to transform image data, or the computeranimation model can be present as associated with an interface describedherein. The modification icons include changes that may be the basis formodifying the user's face within the image or video stream as part ofthe modification operation. Once a modification icon is selected, thetransformation system initiates a process to convert the image of theuser to reflect the selected modification icon (e.g., generate a smilingface on the user). A modified image or video stream may be presented ina graphical user interface displayed on the client device 102 as soon asthe image or video stream is captured, and a specified modification isselected. The transformation system may implement a complexconvolutional neural network on a portion of the image or video streamto generate and apply the selected modification. That is, the user maycapture the image or video stream and be presented with a modifiedresult in real-time or near real-time once a modification icon has beenselected. Further, the modification may be persistent while the videostream is being captured, and the selected modification icon remainstoggled. Machine-taught neural networks may be used to enable suchmodifications.

The graphical user interface, presenting the modification performed bythe transformation system, may supply the user with additionalinteraction options. Such options may be based on the interface used toinitiate the content capture and selection of a particular computeranimation model (e.g., initiation from a content creator userinterface). In various examples, a modification may be persistent afteran initial selection of a modification icon. The user may toggle themodification on or off by tapping or otherwise selecting the face beingmodified by the transformation system and store it for later viewing orbrowse to other areas of the imaging application. Where multiple facesare modified by the transformation system, the user may toggle themodification on or off globally by tapping or selecting a single facemodified and displayed within a graphical user interface. In someexamples, individual faces, among a group of multiple faces, may beindividually modified, or such modifications may be individually toggledby tapping or selecting the individual face or a series of individualfaces displayed within the graphical user interface.

A story table 314 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 306). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client 104 may include an icon that is user-selectableto enable a sending user to add specific content to his or her personalstory.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client 104, based on his or her location. The end resultis a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some examples, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

As mentioned above, the video table 304 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 312 storesimage data associated with messages for which message data is stored inthe entity table 306. The entity table 306 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 312 and the video table 304.

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server118. The content of a particular message 400 is used to populate themessage table 302 stored within the database 126, accessible by themessaging server 118. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 114. A message 400 is shown to include thefollowing example components:

-   -   message identifier 402: a unique identifier that identities the        message 400.    -   message text payload 404: text, to be generated by a user via a        user interface of the client device 102, and that is included in        the message 400.    -   message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from a memory        component of a client device 102, and that is included in the        message 400. Image data for a sent or received message 400 may        be stored in the image table 312.    -   message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400. Video data        for a sent or received message 400 may be stored in the video        table 304.    -   message audio payload 410: audio data, captured by a microphone        or retrieved from a memory component of the client device 102,        and that is included in the message 400.    -   message augmentation data 412: augmentation data (e.g., filters,        stickers, or other annotations or enhancements) that represents        augmentations to be applied to message image payload 406,        message video payload 408, or message audio payload 410 of the        message 400. Augmentation data for a sent or received message        400 may be stored in the augmentation table 310.    -   message duration parameter 414: parameter value indicating, in        seconds, the amount of time for which content of the message        (e.g., the message image payload 406, message video payload 408,        message audio payload 410) is to be presented or made accessible        to a user via the messaging client 104.    -   message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respect to content        items included in the content (e.g., a specific image within the        message image payload 406, or a specific video in the message        video payload 408).    -   message story identifier 418: identifier values identifying one        or more content collections (e.g., “stories” identified in the        story table 314) with which a particular content item in the        message image payload 406 of the message 400 is associated. For        example, multiple images within the message image payload 406        may each be associated with multiple content collections using        identifier values.    -   message tag 420: each message 400 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 406        depicts an animal (e.g., a hon), a tag value may be included        within the message tag 420 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   message sender identifier 422: an identifier (e.g., a messaging        system identifier, email address, or device identifier)        indicative of a user of the client device 102 on which the        message 400 was generated and from which the message 400 was        sent.    -   message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 312.Similarly, values within the message video payload 408 may point to datastored within a video table 304, values stored within the messageaugmentation data 412 may point to data stored in an augmentation table310, values stored within the message story identifier 418 may point todata stored in a story table 314, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within an entity table 306.

FIG. 5 is a diagrammatic representation of a visual search system 500,in accordance with some examples. The visual search system 500 may be acomponent of the image processing server 122 of the messaging serversystem 108. The augmentation system 208 may communicate with the visualsearch system 500 to provide a query video to identify one or morematching marker videos. The augmentation system 208 may activate one ormore augmented reality experiences based on the marker video determinedto match a given query video by the visual search system 500.

The visual search system 500 includes training videos 510 and a markervideo training module 520. The marker video training module 520 includesa feature extraction module 521, a visual word module 522, a visual wordhistogram module 523, a document frequency module 524, a marker videovisual word module 525 and a TF-IDF matrix module 526. The trainingvideos 510 includes a database of maker videos 512 and a database ofdistractor videos 514. The marker videos 512 include a set or collectionof videos with known or predefined sequence of markers present across aspecified number of video frames that trigger one or more augmentedreality experiences. These marker videos includes marks or specifiedtypes of visual attributes that are associated with correspondingaugmented reality experiences. In an example, the marks or specifiedtypes of visual attributes in a given marker video are spread across aconsecutive or non-consecutive number of video frames of each givenmarker video. For example, a first marker video may include a first markor specified type of video attributes in a first frame and a second markor specified type of video attributes in a second frame that is adjacentto the first frame. As another example, a second marker video mayinclude a first mark or specified type of video attributes in a firstframe and a second mark or specified type of video attributes in asecond frame that is non-adjacent to the first frame (the second frameappears after a certain quantity, such as 5 frames, following the firstframe). In some cases, the first mark or specified type of videoattributes and the second mark or specified type of video attributes arethe same and in some cases they are different.

In some cases, the sequence or order in which the marks appear in themarker videos is used to control and trigger the augmented realityexperiences. For example, a first sequence or order of a plurality ofmarks in a first marker video may be associated with and used to triggera first augmented reality experience. A second sequence or order of thesame plurality of marks in a second marker video may be associated withand used to trigger a second augmented reality experience that isdifferent from the first augmented reality experience. When the samemarks are used in marker videos but in different sequences, thecorresponding augmented reality experiences may be different but relatedto each other.

The distractor videos 514 include a set or collection of various randomvideos that are retrieved from the Internet. The distractor videos 514do not include any known marks or features and are not associated withany augmented reality experiences.

During training, the marker video training module 520 accesses a givenvideo from the training videos 510. In one example, the marker videotraining module 520 alternatively retrieves videos from the database ofmaker videos 512 and the database of distractor videos 514. In oneexample, the marker video training module 520 randomly selects videosfrom the database of maker videos 512 and the database of distractorvideos 514. In one example, the marker video training module 520 firstis trained on all or a portion of the database of maker videos 512 andis then further trained based on the database of distractor videos 514.

The feature extraction module 521 processes a given one of the videosthat has been retrieved from the training videos 510. The featureextraction module 521 resizes, scales and crops the retrieved video to acommon size and duration (e.g., the feature extraction module 521 keepsa middle portion of the video having a specified duration, such as 3seconds and discards the rest of the video, in case the video is longerthan the specified duration; or the feature extraction module 521 keepsthe first 3 seconds of the video or other specified duration, such as 5seconds and discards the rest of the video; or the feature extractionmodule 521 keeps the last 3 seconds of the video or other specifiedduration, such as 5 seconds and discards the rest of the video) andgenerates a plurality of features from the given video. For example,feature extraction module 521 generates a plurality of features fromeach frame in the cropped video or a subset of frames (e.g., every otherframe) of the video. The feature extraction module 521 stores a frameidentified or sequence indicator for each subset of the plurality offeatures extracted from each frame. Namely, the feature extractionmodule 521 associates a first set of the plurality of features extractedfrom a first frame of the video with a first frame number or firstsequence indicator and a second set of the plurality of featuresextracted from a second frame of the video with a second frame number orsecond sequence indicator.

In one example, the feature extraction module 521 computes a rootscale-invariant feature transform (SIFT) based on the identifiedplurality of features of the frames of the video. The feature extractionmodule 521 provides the list of features, such as the root SIFT to thevisual word module 522. For example, as shown in FIG. 7 , a set oftraining videos 710 are received and processed by the feature extractionmodule 521. The feature extraction module 521 generates a list offeatures 720 for each frame of the videos 710.

The visual word module 522 trains a k-means index based on the receivedfeatures of each frame. For example, the visual word module 522generates a descriptor to represent a collection of similar features andassigns different collections of the features to the descriptor thatrepresents the collection of features of each frame (or subset offrames) of a video. Specifically, the visual word module 522 assignscentroids to represent visual words (e.g., descriptors) that are presentin the received features of each frame. In an example, the visual wordmodule 522 determines a sequence of the features that are extracted bythe feature extraction module 521. In such cases, the visual word module522 generates a descriptor to represent a collection of similar featuresthat have a particular sequence. The visual word module 522 assignsdifferent collections of the features that have the same particularsequence to the descriptor that represents the collection of features ofeach frame (or subset of frames) of a video having the particularsequence. The visual word module 522 assigns centroids to representvisual words (e.g., descriptors) having a specific sequence that arepresent in the specific sequence in the received features of frames of avideo.

In one example, the visual word module 522 identifies a first collectionof features having a specified sequence that are similar or that arewithin a threshold level of similarity. The visual word module 522determines whether the first collection of features matches a previouslystored cluster of features with the specified sequence in the visualword module 522. If a previously, stored cluster of features with thespecified sequence is found, the first collection of features isassociated with the visual word of the previously stored cluster offeatures. If a previously stored cluster of features with the specifiedsequence is not found (e.g., the previously stored cluster of featuresis similar to the first collection of features but are in a differentsequence, such as they appear in a different order across a set of videoframes), the visual word module 522 creates a new visual word torepresent the first collection of features with the sequence in whichthe features appear and selects a centroid to associate with the firstcollection of features. The visual word module 522 then associates thefirst collection of features with the new visual word that has beencreated. The visual word module 522 continues to process all of thefeatures of the plurality of features based on their respectivesequences in a similar manner until all of the collection of featuresare associated with respective visual words.

For example, as shown in FIG. 6 , a first collection of features 610 isidentified in the received features from the feature extraction module521. The first collection of features 610 can include a particularsequence or combination of the features that are extracted from multipleframes (sequential or non-sequential) of a video. A second collection offeatures 612 is identified in the received features from the featureextraction module 521. The second collection of features 612 can includea particular sequence or combination of the features that are extractedfrom multiple frames (sequential or non-sequential) of the video. Thefeatures in the first collection of features 610 can be the same as thefeatures in the second collection of features 612 but may be in adifferent order. Namely, the features in the first collection offeatures 610 may appear across frames of a video in a different orderthat the features in the second collection of features 612.

The first collection of features 610 all share a common attribute or arewithin a threshold level of similarity of each other. The visual wordmodule 522 determines that a first cluster of features 620, associatedwith a first centroid, is within a threshold level of similarity as thefirst collection of features 610 and corresponds to the same sequence orsimilar sequence as the features in the first collection of features610. For example, the first collection of features 610 can includeFeature 1 (extracted from a first video frame), Feature 2 (extractedfrom a second video frame), and Feature N (extracted from a third videoframe) that all share a common attribute. As such, the sequence offeatures in the first collection of features 610 is Feature 1, thenFeature 2, and then Feature N. The first centroid can include a set offeatures that are within a threshold level of similarity as Features 1,2 and N and have a sequence that substantially resembles the sequence ofFeatures 1, 2 and N. For example, the first centroid can include Feature1, followed by Feature N, and then followed by Feature 2. The firstcentroid can have a sequence that substantially resembles the sequencein a collection when at least half of the features are in the samesequence between the centroid and the collection of features. In suchcircumstances, the visual word module 522 associates the firstcollection of features 610 with the visual word associated with thefirst cluster of features 620.

Similarly, second collection of features 612 all share a commonattribute or are within a threshold level of similarity of each other.The visual word module 522 determines that a second cluster of features622, associated with a second centroid, is within a threshold level ofsimilarity as the second collection of features 612 and corresponds tothe same sequence or similar sequence as the features in the secondcollection of features 612. For example, the second collection offeatures 612 can include Feature 2 (extracted from a first video frame),Feature 4 (extracted from a second video frame), and Feature N(extracted from a third video frame) that all share a common attribute.As such, the sequence of features in the second collection of features612 is Feature 2, then Feature 4, and then Feature N. The secondcentroid can include a set of features that are within a threshold levelof similarity as Features 2, 4 and N and have a sequence thatsubstantially resembles the sequence of Features 2, 4 and N. The secondcentroid can have a sequence that substantially resembles the sequencein a collection when at least half of the features are in the samesequence between the centroid and the collection of features. In suchcircumstances, the visual word module 522 associates the secondcollection of features 612 with the visual word associated with thesecond cluster of features 622.

In some implementations, the visual word module 522 approximates anearest neighbor index for each collection of features using thecentroids of the features. The visual word module 522 assigns the visualwords using the approximate nearest neighbor index.

The visual word histogram module 523 computes the counts of each visualword generated by the visual word module 522 for all of the trainingvideos 510 as each training video is processed by the visual word module522. Specifically, the visual word histogram module 523 generates acount that represents the total number of descriptors (that include orrepresent features in a specified sequence) that correspond to a givenvisual word. This is also referred to as the term frequency. Forexample, after the visual word module 522 assigns the first collectionof features 610 to the first cluster of features 620, the visual wordmodule 522 adds all of the features (descriptors) the first collectionof features 610 to the first cluster of features 620 according to theirsequence in the order in which they appear across frames of thecorresponding video). The visual word histogram module 523 counts howmany features are in the first cluster of features 620 and associatesthat count value with the visual word that is associated with the firstcluster of features 620. As more and more videos are processed and moreand more features of the videos in the same or substantially the samesequence are associated with and added to the first cluster of features620, the count value for the corresponding visual word is increased.

The document frequency module 524 computes the document (video)frequency of each visual word that is generated by the visual wordmodule 522. Specifically, the document frequency module 524 accumulatesa value for each visual word as new training videos are received andinclude features in a sequence that are associated with a particularvisual word. For example, if a training video includes features in aparticular sequence that are included in the first cluster of features620, then the document frequency module 524 increments a value for thevisual word associated with the first cluster of features 620. If thesame training video does not includes features that are included in thesecond cluster of features 622, then the document frequency module 524does not increment a value for the visual word associated with thesecond cluster of features 622. In this way, the document frequencymodule 524 computes values representing how many of the training videos510 include the visual words that are generated by the visual wordmodule 522. As each new training video is processed, a subset of valuesassociated respectively with the visual words generated by the visualword module 522 are incremented once if the features of the new trainingvideo are associated with clusters of features associated with therespective visual words.

The marker image visual word module 525 retrieves a set of or all of themarker videos 512. In some cases, the marker video visual word module525 retrieves all of the features and their corresponding sequencesgenerated based only on the marker videos 512. These features areprovided to the TF-IDF matrix module 526 to create a visual wordcodebook or visual search database. For example, the TF-IDF matrixmodule 526 uses only the marker videos provided by the marker videovisual word module 525 to create a sparse matrix representing the TF-IDFof each visual word. As an example, the TF-IDF matrix module 526 assignsweights to visual words based on their overall importance based on themarker videos 512. The weight of a given visual word increaseproportionally to the number of times the features associated with thegiven visual word appear in the marker videos 512 but is offset by thefrequency of the visual word in the corpus of marker videos 512. ThisTF-IDF matrix is used during a search based on a query video to identifyone or more matching marker videos. In some cases, the square root ofeach of the features or terms is used as the term frequency of theTF-IDF to account for descriptor burstiness.

As an example, the TF-IDF weight of each visual word is composed by twoterms: the first computes the normalized Term Frequency (TF)representing the number of times a visual word (that includes a givensequence of similar features) appears in a given one of the trainingvideos 510, divided by the total number of visual words in that giventraining video. The second term is the Inverse Document Frequency (IDF),computed as the logarithm of the total number of the marker videos 512,provided by the marker video visual word module 525, divided by thenumber of marker videos 512 where the specific visual word (including aparticular sequence of features) appears. Specifically, TF: TermFrequency, measures how frequently a visual term occurs in a trainingvideo. IDF measures how important a visual word is. While computing TF,all terms are considered equally important. For example, as shown inFIG. 7 , the TF-IDF of each visual word is shown for each one of thevideos 710. Specifically, a first TF-IDF 730 represents the TF-IDF valueof each visual word in a set of visual words for a first training video.A second TF-IDF 732 represents the TF-IDF value of each visual word inthe set of visual words for a second training video.

The visual search system 500 can be used to identify a set of matchingvideos given a query video. For example, the visual search system 500may receive a query video from a client device 102. The visual searchsystem 500 performs video pre-processing and normalization of the queryvideo to convert the image to a common size, duration and orientation(e.g., the visual search system 500 can crop the received video to havea specified duration, such as 3 seconds, in case the received video hasa duration that exceeds the specified duration). The visual searchsystem 500 can crop the beginning portion, ending portion or middleportion of the received video to generate the query video having thespecified duration.

The visual search system 500 computes a set of features for the queryvideo, such as by calculating a root SIFT descriptor and keypoints. Forexample, the visual search system 500 extracts features that appearacross a set of frames (sequential or non-sequential) and determines asequence of the different sets of the extracted features. In some cases,each sequence can include a minimum and/or maximum number of features.In such cases, the visual search system 500 generates multiple sequencesthat include different combinations of features that are extracted frommultiple frames of the query video.

The visual search system 500 accesses the visual word codebook generatedpreviously by the visual word module 522. The visual search system 500also accesses the document frequency for each of the visual words fromthe document frequency module 524. The visual search system 500 computesa visual word histogram for the different sequences of features of thequery video and then calculates a TF-IDF vector for the query video.

For example, the visual search system 500 identifies a first cluster offeatures that are similar to (that are nearest to) and havesubstantially the same sequence as a first sequence of features of thequery video. In response to identifying the first cluster of features,the visual search system 500 assigns the first sequence of features ofthe query video to the visual word associated with the first cluster offeatures. The visual search system 500 identifies a second cluster offeatures that are similar to (that are nearest to) and havesubstantially the same sequence as a second sequence of features of thequery video. In response to identifying the second cluster of features,the visual search system 500 assigns the second sequence of features ofthe query video to the visual word associated with the second cluster offeatures. The visual search system 500 continues processing all of theextracted features and sequences of the features of the query video toidentify all of the visual words associated with the query video.

The visual search system 500 computes the TF-IDF vector for the queryvideo and searches the TF-IDF matrix to obtain the top candidatematching marker videos. In some cases, the visual search system 500generates a count of the sequences of features of the query video thatis associated with each visual word (e.g., one count for each visualword) and offsets that count by the document frequency obtained from thedocument frequency module 524 to determine the TF-IDF value for eachvisual word. In some embodiments, the visual search system 500 performsa cosine similarity of the identified visual words that are associatedwith the query video and the visual search database TF-IDF matrix toidentify the top candidate matching marker video.

After identifying a set of matching marker videos, the visual searchsystem 500 performs a geometric verification to rank the matching markervideo. For example, the visual search system 500 first performs a Lowe'sratio test to compare a ratio of principal curves of the plurality ofmatching videos and the received video to a threshold. Then the visualsearch system 500 performs a random sample consensus (RANSAC) operationto verify the key points and then uses the number of inliers as thematching candidate score to re-rank the candidate marker videos. Forexample, as shown in FIG. 8 , a first frame 810 and a second frame 830(adjacent or non-adjacent to the first frame 810) of a given query videoare processed to identify a sequence of features. After identifying aset of matching marker videos and performing the geometric verification,a top ranked marker video that includes frames 820 and 840 having thesame or substantially the same sequence of features is identified andselected. The sequence of features in the query video matches thesequence of features of the marker video, as shown by the linesextending from each feature of the first frame 810 in the query video tothe corresponding features of the first frame 820 of the marker videoand as shown by the lines extending from each feature of the secondframe 830 in the query video to the corresponding features of the secondframe 840 of the marker video. The sequence in which the features in thefirst frame 810 are followed by the features in the second frame 830 isthe same as the sequence of features in the first frame 820 followed bythe sequence of features in the second frame 840.

In some cases, a first subset of the features in the first frame 810being followed by a first subset of the features in the second frame 830forms a first sequence of features. A second subset of the features inthe first frame 810 being followed by a second subset of the features inthe second frame 830 forms a second sequence of features. The firstsequence of features is determined to match the same sequence of thesubset of features from the first frame 820 to the second frame 840. Insuch cases, a determination as to whether the second sequence offeatures also matches the sequence of the subset of features from thefirst frame 820 to the second frame 840 is made. If both the firstsequence of some of the features from the first frame 810 and some ofthe features from the second frame 830 and the second sequence of someother set of features from the first frame 810 and some other set offeatures from the second frame 830 match, correspond to or aresubstantially the same as the sequence of corresponding features in theframes 820 and 840 of marker video, the marker video is selected asmatching the query video. In such cases, the augmented realityexperience associated with the marker video is accessed and launched.

In some embodiments, the visual search system 500 searches all of thedatabase marker videos based on the query video in a brute force manner.This is done without using the TF-IDF matrix and simply compares thequery video against all of the marker videos stored in the database.Once a set of marker videos is identified in the brute force manner, thevisual search system 500 performs a geometric verification to rank thematching marker videos.

Once a top ranked marker video is determined to match, a correspondingaugmented reality experience corresponding to the marker video isaccessed. The augmented reality experience is activated and providedback to the client device 102 to augment an image or video that isdisplayed by the client device 102.

FIGS. 9A-F, are diagrammatic representations of graphical userinterfaces, in accordance with some examples. Specifically, a messagingclient 104 on a client device 102 displays a camera feed of a user'senvironment. The messaging client 104 continuously or periodicallytransmits one or more video clips to the visual search system 500 toidentify one or more augmented reality experiences associated withsequences of features present in the one or more video clips. In someembodiments, the messaging client 104 transmits one or more video clipsto the visual search system 500 to identify one or more augmentedreality experiences associated with sequences of features present in theone or more video clips in response to receiving input from the userthat presses and holds a finger for a threshold period of time on thescreen displaying the one or more images.

For example, the one or more video clips or videos captured by theclient device 102 may include an object 910 (e.g., an image of a makeuppalate that includes a plurality of makeup colors) (FIG. 9A). The visualsearch system 500 extracts a plurality of features of the object 910that appear across a subset of frames to form a sequence of features.The visual search system 500 searches a visual search database toidentify one or more marker videos. In an embodiment, the visual searchsystem 500 generates a TF-IDF vector based on visual words correspondingto the extracted sequence of features of the object 910 and compares theTF-IDF vector against TF-IDF vectors stored in the visual searchdatabase. The visual search system 500 ranks the identified matchingvideos based on performing geometric verifications of the identifiedmatching videos. The visual search system 500 selects an augmentedreality experience that is associated with a selected one of theidentified matching video (e.g., the top ranked matching video). Thevisual search system 500 provides the augmented reality experience tothe client device 102 or instructs the client device 102 to activate theparticular augmented reality experience. As an example, as shown in FIG.9A, the augmented reality experience includes the presentation ofgraphical elements 912 and 922 associated with the makeup palate that isscanned as the object 910. The messaging application 104 presents a listof augmented reality experiences and visually identifies with anindicator 923 the augmented reality experience that has beenautomatically activated and selected based on a match detected by thevisual search system 500.

In some implementations, the object 910 is captured in a video by arear-facing camera of the client device 102. In response to theaugmented reality experience being activated that is associated with theobject 910, the augmented reality experience instructs the client device102 to automatically activate the front-facing camera instead of therear-facing camera. This may cause images of the user's face to appearin the user interface 900. The graphical elements associated with theobject 910 are then overlaid on top of the user's face images capturedby the front-facing camera. Input from the user can manipulate thegraphical elements to modify which portions of the user's face arecovered by the graphical elements. Specifically, the object 910 may be amakeup palate. The augmented reality experience associated with object910 may include presentation of a set of graphical elements thatrepresent a set of makeup colors associated with the makeup palate. Theuser can tap on different regions of the makeup palate object 910depicted in the camera view as a graphical element to select a makeupcolor.

In response to receiving a first input that taps a first portion ofobject 910, a first set of graphical elements 912 corresponding to afirst makeup color of the plurality of makeup colors are selected andpresented on the face depicted in the user interface 900. In response toreceiving a second input (following the first input) that taps a secondportion of object 910, a second set of graphical elements 922corresponding to a second makeup color of the plurality of makeup colorsare selected and presented on the face depicted in the user interface900. In some cases, when the second input is received, the userinterface is divided into two regions and the face is depicted in bothregions. The user can simultaneously apply the first set of graphicalelements 912 to the face depicted in a top region by touching differentportions of the face depicted in the top region. The user cansimultaneously apply the second set of graphical elements 922 to theface depicted in a bottom region by touching different portions of theface depicted in the bottom region. In this way, the user can see howdifferent makeup colors look on the user simultaneously.

Once the user is satisfied with the application of the graphicalelements to the face depicted in the user interface 900, the user canselect an option to purchase the makeup palate corresponding to theobject 910. The user can also capture an image or video of the usermodifying the face with the different graphical elements and share theimage or video with one or more friends.

As another example, the one or more videos or video dips captured by theclient device 102 may include an object 991 (e.g., an image of acomputer screen that displays a webpage that includes a logo of aparticular team) (FIG. 9B). The visual search system 500 extracts aplurality of features of the object 991 that appear across many framesof the video to generate a sequence of features. The visual searchsystem 500 searches a visual search database to identify one or moremarker videos. While the visual search system 500 performs the scan andextraction of the sequence of features, a progress indicator (such asthe word “scanning” or a set of music icons) is displayed on the userinterface of the client device 102.

In an embodiment, the visual search system 500 generates a TF-IDF vectorbased on visual words corresponding to the extracted sequence offeatures of the object 991 and compares the TF-IDF vector against TF-IDFvectors stored in the visual search database. The visual search system500 ranks the identified matching videos based on performing geometricverifications of the identified matching videos. The visual searchsystem 500 selects an augmented reality experience that is associatedwith a selected one of the identified matching videos (e.g., the topranked matching video). The visual search system 500 provides theaugmented reality experience to the client device 102 or instructs theclient device 102 to activate the particular augmented realityexperience. As an example, as shown in FIG. 9B, the augmented realityexperience includes the presentation of a user interface 924 with agraphical element (e.g., an avatar of a player on the particular team)associated with the logo of the particular team that is scanned as theobject 991. While the graphical element is presented in user interface924, the background video that was used in the search is dimmed toenhance or call attention to the graphical element that is displayed.The messaging application 104 presents a list of augmented realityexperiences and visually identifies with an indicator 926 the augmentedreality experience that has been automatically activated and selectedbased on a match detected by the visual search system 500.

The user can interact with the graphical element displayed in userinterface 924. For example, the user can select between a plurality ofoptions to change the team member that is represented by the avatar inthe user interface 924. The user can rotate the avatar and select anoption to cause the user's face to be presented as the avatar of theteam member. This creates an experience in which the user's face isdisplayed on an avatar that corresponds to a team member of theparticular team that was scanned in as object 991. The user can selectan option to capture an image or video of the user interacting with theavatar presented in the user interface 924 and share the image or videowith one or more friends.

As another example, the one or more videos captured by the client device102 may include an object 932 (e.g., an image of a drink container)(FIG. 9C) that appears across multiple frames of the video. The visualsearch system 500 extracts a sequence of features of the object 932 andsearches a visual search database to identify one or more marker videos.While the visual search system 500 performs the scan and extraction ofthe sequence of features, a progress indicator (such as the word“scanning” or a set of music icons) is displayed on the user interfaceof the client device 102.

In an embodiment, the visual search system 500 generates a TF-IDF vectorbased on visual words corresponding to the extracted sequence offeatures of the object 932 and compares the TF-IDF vector against TF-IDFvectors stored in the visual search database. The visual search system500 ranks the identified matching videos based on performing geometricverifications of the identified matching videos. The visual searchsystem 500 selects an augmented reality experience that is associatedwith a selected one of the identified matching videos (e.g., the topranked matching video). The visual search system 500 provides theaugmented reality experience to the client device 102 or instructs theclient device 102 to activate the particular augmented realityexperience. As an example, as shown in FIG. 9C, the augmented realityexperience includes the presentation of a user interface with agraphical elements 934 (e.g., holiday related graphical elements)associated with the drink container that is scanned as the object 932.

Specifically, the visual search system 500 determines that the matchingmarker video for the drink container is associated with a plurality ofdifferent augmented reality experiences. The visual search system 500determines that the current date is within a threshold time of aparticular holiday (e.g., is within a week of the holiday) or event(e.g., a sporting event, such as the Superbowl). In response, the visualsearch system 500 selects one of the plurality of augmented realityexperiences that is determined to be associated with the particularholiday or event to provide as the selected augmented reality experienceto the client device 102. The augmented reality experience that isassociated with the holiday or event may include one or more graphicalelements that represent or that are associated with the holiday orevent. The messaging application 104 presents a list of augmentedreality experiences and visually identifies with an indicator 936 theaugmented reality experience that has been automatically activated andselected based on a match detected by the visual search system 500.

The user can interact with the graphical elements 934 displayed in theuser interface. For example, the user can select between a plurality ofoptions to change the types of holiday graphical elements or eventgraphical elements that are overlaid on the screen. In some cases, theobject 932 is captured by the rear-facing camera and the augmentedreality experience automatically activates the front-facing camera todisplay the graphical elements on the video captured by the front-facingcamera. For example, the user can capture a video of the drink containerusing the rear-facing camera. In response to the visual search system500 providing the augmented reality experience associated with the drinkcontainer, the client device 102 automatically switches to thefront-facing camera to augment an image of the user's face with thegraphical elements representing the holiday or event. The user canselect an option to capture an image or video of the user interactingwith the graphical elements 934 presented in the user interface andshare the image or video with one or more friends.

As another example, the one or more videos captured by the client device102 may include an object 942 (e.g., an image of a commuter card, suchas a railway or airplane ticket) (FIG. 9D). The visual search system 500extracts a sequence of features of the object 942 and searches a visualsearch database to identify one or more marker videos. While the visualsearch system 500 performs the scan and extraction of the sequence offeatures, a progress indicator (such as the word “scanning” or a set ofmusic icons) is displayed on the user interface of the client device102.

In an embodiment, the visual search system 500 generates a TF-IDF vectorbased on visual words corresponding to the extracted sequence offeatures of the object 942 and compares the TF-IDF vector against TF-IDFvectors stored in the visual search database. The visual search system500 ranks the identified matching videos based on performing geometricverifications of the identified matching videos. The visual searchsystem 500 selects an augmented reality experience that is associatedwith a selected one of the identified matching videos (e.g., the topranked matching video). The visual search system 500 provides theaugmented reality experience to the client device 102 or instructs theclient device 102 to activate the particular augmented realityexperience. As an example, as shown in FIG. 9D, the augmented realityexperience includes the presentation of a user interface with agraphical elements 944 (e.g., a three-dimensional presentation of acommuter map, such as an interactive railway map) associated with thecommuter card that is scanned as the object 942.

Specifically, in some embodiments, the visual search system 500 stores aplurality of different augmented reality experiences representingdifferent commuter systems (public transit modes of transportation),such as different railway maps for different regions, different subwaymaps for different regions, different train maps for different regions,different bus maps for different regions. Each of the commuter basedaugmented reality experiences is associated with a respective markervideo of a commuter card associated with the given region. For example,a first subway map that includes one or more graphical elementsrepresenting a first subway system is associated with a first markervideo of a first subway card that can be used to access the first subwaysystem in the first region. A second subway map that includes one ormore graphical elements representing a second subway system isassociated with a second marker video of a second subway card that canbe used to access the second subway system in the second region. Inresponse to the visual search system 500 matching the object 942 to thesecond marker video, the visual search module causes the client device102 to activate the augmented reality experience in which the one ormore graphical elements representing the second subway system isdisplayed. The one or more graphical elements may be displayed as anoverlay on top of the image of the subway card captured as the object942. The one or more graphical elements can be animated to represent acurrent location of a train (public transit vehicle).

The messaging application 104 presents a list of augmented reality,experiences and visually identifies with an indicator 946 the augmentedreality experience that has been automatically activated and selectedbased on a match detected by the visual search system 500. The user caninteract with the graphical elements 944 displayed in the userinterface. For example, the user can tap one of the graphical elementsthat are displayed and station information is presented that correspondsto the station represented by the tapped graphical element. In someembodiments, the client device 102 accesses a search history orconversation history of the user to predict or determine a destinationfor the user. Based on the determined destination, the client device 102displays a visual indicator or visually distinguishes one of thegraphical elements 944 that corresponds to a station associated with thedetermined destination. This alerts the user as to which station theuser needs to reach to arrive at the destination. The client device 102may determine a current location for the user and visually distinguishone of the graphical elements 944 that corresponds to a stationassociated with the current location. The graphical elementcorresponding to the current location may be displayed using differentvisual properties (e.g., with a different visual indicator or may bevisually distinguished) from the station corresponding to thedestination. For example, a green circle may be placed around thegraphical element 944 corresponding to the station closest to the user'scurrent location and a red circle may be placed around the graphicalelement 944 corresponding to the station closest to the destination.

As another example, the one or more videos captured by the client device102 may include an object 952 (e.g., an image of a currency) (FIG. 9E).The visual search system 500 extracts a sequence of features of theobject 952 depicted across various frames of the video and searches avisual search database to identify one or more marker videos. While thevisual search system 500 performs the scan and extraction of thesequence of features, a progress indicator (such as the word “scanning”or a set of music icons) is displayed on the user interface of theclient device 102.

In an embodiment, the visual search system 500 generates a TF-IDF vectorbased on visual words corresponding to the extracted features of theobject 952 and compares the TF-IDF vector against TF-IDF vectors storedin the visual search database. The visual search system 500 ranks theidentified matching videos based on performing geometric verificationsof the identified matching videos. The visual search system 500 selectsan augmented reality experience that is associated with a selected oneof the identified matching videos (e.g., the top ranked matching video).The visual search system 500 provides the augmented reality experienceto the client device 102 or instructs the client device 102 to activatethe particular augmented reality experience. As an example, as shown inFIG. 9E, the augmented reality experience includes the presentation of auser interface with one or more graphical elements 954 (e.g., athree-dimensional presentation of a research and development icon, suchas a microscope). The one or more graphical elements 954 may represent acharity that matches a profile of the user of the client device 102. Asanother example, as shown in FIG. 9E, the augmented reality experienceincludes the presentation of a user interface with another set of one ormore graphical elements 956 (e.g., a three-dimensional presentation of ahospital).

In one embodiment, the graphical elements 954 may animate out of viewafter a specified period of time and the graphical elements 956 may beanimated into view on top of the image of the currency. For example, thevisual search system 500 may indicate the position within the screen ofthe currency that is depicted. The client device 102 may position thegraphical elements 954 and 956 on top of the position of the currency.

Specifically, in some embodiments, the visual search system 500 stores aplurality of different augmented reality experiences representingdifferent charities. Each of the charity based augmented realityexperiences is associated with a respective marker video of a currency.For example, a first charity that includes one or more graphicalelements representing the first charity is associated with a firstmarker video of a first currency (e.g., a type of currency, a value ofthe currency, or a region associated with the currency). A secondcharity that includes one or more graphical elements representing thesecond charity is associated with a second marker video of a secondcurrency (e.g., a type of currency, a value of the currency, or a regionassociated with the currency). In response to the visual search system500 matching the object 952 to the second marker video, the visualsearch module causes the client device 102 to activate the augmentedreality experience in which the one or more graphical elementsrepresenting the second charity is displayed.

In some embodiments, the visual search system 500 stores a plurality ofdifferent augmented reality experiences representing differentcurrencies (e.g., values or types of currency). Each of the charitybased augmented reality experiences is associated with a respectivemarker video of a currency type or value. For example, a first charityincludes a first set of graphical elements representing the firstcharity that is associated with a first marker video of a firstcurrency. A second set of graphical elements representing the firstcharity is associated with a second marker video of a second currency.In response to the visual search system 500 matching the object 952 tothe second marker video, the visual search module causes the clientdevice 102 to activate the augmented reality experience in which thesecond set of graphical elements representing the first charity isdisplayed.

In some embodiments, a plurality of charities may be associated with agiven marker video. The visual search system 500 ranks the plurality ofcharities associated with the given marker video based on profileinformation of the user of the client device 102. In this way, the topranked charity that is of greatest interest to the user may berepresented to the user by a respective augmented reality experience inthe client device 102 in response to the user capturing a video ofcurrency. The visual search system 500 may alternatively or additionallyrank the plurality of charities associated with the given marker videobased on friends of the user of the client device 102 on the messagingapplication 104. In this way, the top ranked charity that is of greatestinterest to friends of the user may be represented to the user by arespective augmented reality experience in the client device 102 inresponse to the user capturing a video of currency. For example, if oneof the user's friends previously donated money to the particularcharity, that charity may be selected to be represented by the augmentedreality experience on the client device 102.

The user can interact with the graphical elements 944 displayed in theuser interface. For example, the user can tap one of the graphicalelements that are displayed and a website 958 is accessed and ispresented that corresponds to the charity represented by the tappedgraphical element. The website 958 may allow the user to select betweena plurality of options to donate money to the charity. In someimplementations, the value of the currency depicted as the object 952 isdetermined by the visual search system 500. The visual search system 500may instruct the client device 102 to automatically highlight or selectan option to donate money to the charity based on the determined valueof the currency. For example, if the user scans a video of a one hundreddollar bill, the website 958 may automatically highlight the option todonate one hundred dollars to the charity associated with the graphicalelements 954. In response to the user completing a donation to thecharity, the one or more graphical elements representing the charity maybe animated or change to indicate that the user completed the donationto the charity.

FIG. 10 is a flowchart illustrating example operations of the messagingclient 104 in performing process 1000, according to example embodiments.The process 1000 may be embodied in computer-readable instructions forexecution by one or more processors such that the operations of theprocess 1000 may be performed in part or in whole by the functionalcomponents of the messaging server system 108; accordingly, the process1000 is described below by way of example with reference thereto.However, in other embodiments at least some of the operations of theprocess 1000 may be deployed on various other hardware configurations.The operations in the process 1000 can be performed in any order, inparallel, or may be entirely skipped and omitted.

At operation 1001, the image processing server 122 identifies aplurality of features for frames of a video received by a messagingapplication server, as discussed above.

At operation 1002, the image processing server 122 assigns a firstsequence of a first subset of the plurality of features and a secondsequence of a second subset of the plurality of features respectively toa first nearest visual codebook cluster and a second nearest visualcodebook cluster, as discussed above.

At operation 1003, the image processing server 122 applies the first andsecond nearest visual codebook clusters to a visual search database toidentify a plurality of candidate matching videos, as discussed above.

At operation 1004, the image processing server 122 selects a givenmatching video from the plurality of candidate matching videos based ona rank representing similarity of the given matching video to the videoreceived by the messaging application server, as discussed above.

At operation 1005, the image processing server 122 accesses an augmentedreality experience corresponding to the given matching video, asdiscussed above.

Machine Architecture

FIG. 11 is a diagrammatic representation of the machine 1100 withinwhich instructions 1108 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 1100to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 1108 may cause the machine 1100to execute any one or more of the methods described herein. Theinstructions 1108 transform the general, non-programmed machine 1100into a particular machine 1100 programmed to carry out the described andillustrated functions in the manner described. The machine 1100 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 1100 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1100 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smartphone, a mobiledevice, a wearable device (e.g., a smartwatch), a smart home device(e.g., a smart appliance), other smart devices, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing the instructions 1108, sequentially or otherwise,that specify actions to be taken by the machine 1100. Further, whileonly a single machine 1100 is illustrated, the term “machine” shall alsobe taken to include a collection of machines that individually orjointly execute the instructions 1108 to perform any one or more of themethodologies discussed herein. The machine 1100, for example, maycomprise the client device 102 or any one of a number of server devicesforming part of the messaging server system 108. In some examples, themachine 1100 may also comprise both client and server systems, withcertain operations of a particular method or algorithm being performedon the server-side and with certain operations of the particular methodor algorithm being performed on the client-side.

The machine 1100 may include processors 1102, memory 1104, andinput/output (I/O) components 1138, which may be configured tocommunicate with each other via a bus 1140. In an example, theprocessors 1102 (e.g., a Central Processing Unit (CPU), a ReducedInstruction Set Computing (RISC) Processor, a Complex Instruction SetComputing (CISC) Processor, a Graphics Processing Unit (GPU), a DigitalSignal Processor (DSP), an Application Specific Integrated Circuit(ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor,or any suitable combination thereof) may include, for example, aprocessor 1106 and a processor 1110 that execute the instructions 1108.The term “processor” is intended to include multi-core processors thatmay comprise two or more independent processors (sometimes referred toas “cores”) that may execute instructions contemporaneously. AlthoughFIG. 11 shows multiple processors 1102, the machine 1100 may include asingle processor with a single-core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiples cores, or any combinationthereof.

The memory 1104 includes a main memory 1112, a static memory 1114, and astorage unit 1116, all accessible to the processors 1102 via the bus1140. The main memory 1104, the static memory 1114, and the storage unit1116 store the instructions 1108 embodying any one or more of themethodologies or functions described herein. The instructions 1108 mayalso reside, completely or partially, within the main memory 1112,within the static memory 1114, within machine-readable medium 1118within the storage unit 1116, within at least one of the processors 1102(e.g., within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1100.

The I/O components 1138 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1138 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1138 mayinclude many other components that are not shown in FIG. 11 . In variousexamples, the I/O components 1138 may include user output components1124 and user input components 1126. The user output components 1124 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 1126 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 1138 may include biometriccomponents 1128, motion components 1130, environmental components 1132,or position components 1134, among a wide array of other components. Forexample, the biometric components 1128 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 1130 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 1132 include, for example, one or cameras(with still image/photograph and video capabilities), illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 360° camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera, and a depth sensor, for example.

The position components 1134 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1138 further include communication components 1136operable to couple the machine 1100 to a network 1120 or devices 1122via respective coupling or connections. For example, the communicationcomponents 1136 may include a network interface component or anothersuitable device to interface with the network 1120. In further examples,the communication components 1136 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1122 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 1136 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1136 may include Radio Frequency Identification(UM) tag reader components, NFC smart tag detection components, opticalreader components (e.g., an optical sensor to detect one-dimensional barcodes such as Universal Product Code (UPC) bar code, multi-dimensionalbar codes such as Quick Response (QR) code, Aztec code, Data Matrix,Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and otheroptical codes), or acoustic detection components (e.g., microphones toidentify tagged audio signals). In addition, a variety of informationmay be derived via the communication components 1136, such as locationvia Internet Protocol (IP) geolocation, location via Wi-Fi® signaltriangulation, location via detecting an NFC beacon signal that mayindicate a particular location, and so forth.

The various memories (e.g., main memory 1112, static memory 1114, andmemory of the processors 1102) and storage unit 1116 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 1108), when executedby processors 1102, cause various operations to implement the disclosedexamples.

The instructions 1108 may be transmitted or received over the network1120, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components1136) and using any one of several well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions 1108may be transmitted or received using a transmission medium via acoupling (e.g., a peer-to-peer coupling) to the devices 1122.

Software Architecture

FIG. 12 is a block diagram 1200 illustrating a software architecture1204, which can be installed on any one or more of the devices describedherein. The software architecture 1204 is supported by hardware such asa machine 1202 that includes processors 1220, memory 1226, and I/Ocomponents 1238. In this example, the software architecture 1204 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1204 includes layerssuch as an operating system 1212, libraries 1210, frameworks 1208, andapplications 1206. Operationally, the applications 1206 invoke API calls1250 through the software stack and receive messages 1252 in response tothe API calls 1250.

The operating system 1212 manages hardware resources and provides commonservices. The operating system 1212 includes, for example, a kernel1214, services 1216, and drivers 1222. The kernel 1214 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1214 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1216 canprovide other common services for the other software layers. The drivers1222 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1222 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., USB drivers), WI-FI®drivers, audio drivers, power management drivers, and so forth.

The libraries 1210 provide a common low-level infrastructure used by theapplications 1206. The libraries 1210 can include system libraries 1218(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1210 can include APIlibraries 1224 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries SQLite to provide various relational database functions), weblibraries (e.g., WebKit to provide web browsing functionality), and thelike. The libraries 1210 can also include a wide variety of otherlibraries 1228 to provide many other APIs to the applications 1206.

The frameworks 1208 provide a common high-level infrastructure that isused by the applications 1206. For example, the frameworks 1208 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 1208 canprovide a broad spectrum of other APIs that can be used by theapplications 1206, some of which may be specific to a particularoperating system or platform.

In an example, the applications 1206 may include a home application1236, a contacts application 1230, a browser application 1232, a bookreader application 1234, a location application 1242, a mediaapplication 1244, a messaging application 1246, a game application 1248,and a broad assortment of other applications such as a externalapplication 1240. The applications 1206 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 1206, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the external application1240 (e.g., an application developed using the ANDROID™ or IOS™ softwaredevelopment kit (SDK) by an entity other than the vendor of theparticular platform) may be mobile software running on a mobileoperating system such as IOS™, ANDROID™, WINDOWS® Phone, or anothermobile operating system. In this example, the external application 1240can invoke the API calls 1250 provided by the operating system 1212 tofacilitate functionality described herein.

Glossary

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data. Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions.

Components may constitute either software components (e.g., codeembodied on a machine-readable medium) or hardware components. A“hardware component” is a tangible unit capable of performing certainoperations and may be configured or arranged in a certain physicalmanner. In various example embodiments, one or more computer systems(e.g., a standalone computer system, a client computer system, or aserver computer system) or one or more hardware components of a computersystem (e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwarecomponent that operates to perform certain operations as describedherein.

A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as afield-programmable gate array (FPGA) or an application specificintegrated circuit (ASIC). A hardware component may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein.

Considering embodiments in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors 1102 orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processors orprocessor-implemented components may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented components may be distributed across a number ofgeographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo and the like. The access time for the ephemeral message may be setby the message sender. Alternatively, the access time may be a defaultsetting or a setting specified by the recipient. Regardless of thesetting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM). FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under e term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

Changes and modifications may be made to the disclosed embodimentswithout departing from the scope of the present disclosure. These andother changes or modifications are intended to be included within thescope of the present disclosure, as expressed in the following claims.

What is claimed is:
 1. A method comprising: identifying a plurality offeatures for frames of a video received by an application server;assigning a first sequence of a first subset of the plurality offeatures and a second sequence of a second subset of the plurality offeatures respectively to a first visual codebook cluster and a secondvisual codebook cluster; applying the first and second visual codebookclusters to a visual search database to identify a plurality ofcandidate matching videos, the plurality of candidate matching videosbeing identified in response to computing cosine similarity between termfrequency information associated with the video and term frequencyinformation associated with a plurality of marker videos, the pluralityof candidate matching videos being a first subset of the plurality ofmarker videos having a greater cosine similarity than a second subset ofthe plurality of marker videos; selecting a given matching video fromthe plurality of candidate matching videos based on a rank representingsimilarity of the given matching video to the video received by theapplication server; and accessing an augmented reality experiencecorresponding to the given matching video.
 2. The method of claim 1,further comprising computing a root scale-invariant feature transform(SIFT) based on the identified plurality of features of the video. 3.The method of claim 1, further comprising: identifying, based onsequences of different subsets of the plurality of features, a pluralityof descriptors and corresponding key points for the video; and applyingthe plurality of descriptors and the corresponding key points to avisual word codebook and document frequency index, generated based ontraining videos, to compute a visual word histogram and the termfrequency information comprising a term frequency-inverse documentfrequency (TF-IDF) vector for the video.
 4. The method of claim 3,wherein the first sequence of the first subset of the plurality offeatures is assigned to the first visual codebook cluster based on thevisual word histogram.
 5. The method of claim 3, wherein applying thefirst and second visual codebook clusters to the visual search databasecomprises: accessing a visual search database that represents a TF-IDFvector of visual words for a plurality of marker videos, wherein theterm frequency information associated with the video comprises theTF-IDF vector of the video and the term frequency information associatedwith the plurality of marker videos comprises the TF-IDF vector of theplurality of marker videos.
 6. The method of claim 1, wherein a firstmarker video of the plurality of candidate matching videos comprises afirst sequence of markers across a subset of frames of the first markervideo, and wherein a second marker video of the plurality of markervideos comprises a second sequence of markers across a subset of framesof the second marker video.
 7. The method of claim 1, furthercomprising: obtain a set of training videos comprising marker videos anddistractor videos; and calculating a root scale-invariant featuretransform (SIFT) vector comprising descriptors and key points for theset of training videos.
 8. The method of claim 7, wherein the markervideos comprise videos having a sequence of predefined objects that isassociated with a respective augmented reality experience, and whereinthe distractor videos comprise random videos that are publiclyavailable.
 9. The method of claim 7, further comprising resizing andcropping a duration of the set of training videos to correspond to acommon size and common duration.
 10. The method of claim 7, furthercomprising: training a k-means index based on the root SIFT vector,wherein a plurality of centroids of the k-means index representrespectively a plurality of visual words.
 11. The method of claim 10,further comprising: identifying a first closest centroid of theplurality of centroids for a first sequence of key points of a giventraining video; assigning a first descriptor corresponding to the firstsequence of key points to the first closest centroid representing afirst visual word; identifying a second closest centroid of theplurality of centroids for a second sequence of key points of the giventraining video; and assigning a second descriptor corresponding to thesecond sequence of key points to the second closest centroidrepresenting a second visual word.
 12. The method of claim 11, furthercomprising generating a visual word codebook comprising the first visualcodebook cluster and the second visual codebook cluster using theplurality of centroids.
 13. The method of claim 11, further comprising:computing count values for each of the plurality of visual words foreach of the set of training videos, each of the count values representsa total number of descriptors in a training video of the set of trainingvideos that is assigned to a given one of the plurality of visual words.14. The method of claim 13, further comprising: computing a documentfrequency of each of the plurality of visual words that representsfrequency of the plurality of visual words across the set of trainingvideos.
 15. The method of claim 14, further comprising: obtaining themarker videos from the set of training videos; and generating, based onthe computed document frequency, a sparse matrix representing a termfrequency-inverse document frequency (TF-IDF) vector of each of theplurality of visual words associated with the descriptors of the markervideos.
 16. The method of claim 15, wherein a first video of the set oftraining videos comprises a first sequence of a given set of features,and wherein a second video of the set of training videos comprises asecond sequence of the given set of features.
 17. The method of claim16, wherein the first video is associated with a first augmented realityexperience and the second video is associated with a second augmentedreality experience that is related to the first augmented realityexperience.
 18. The method of claim 1, wherein each of the plurality ofcandidate matching videos is associated with a different type ofaugmented reality experience.
 19. A system comprising: a processorconfigured to perform operations comprising: identifying a plurality offeatures for frames of a video received by an application server;assigning a first sequence of a first subset of the plurality offeatures and a second sequence of a second subset of the plurality offeatures respectively to a first visual codebook cluster and a secondvisual codebook cluster; applying the first and second visual codebookclusters to a visual search database to identify a plurality ofcandidate matching videos, the plurality of candidate matching videosbeing identified in response to computing cosine similarity between termfrequency information associated with the video and term frequencyinformation associated with a plurality of marker videos, the pluralityof candidate matching videos being a first subset of the plurality ofmarker videos having a greater cosine similarity than a second subset ofthe plurality of marker videos; selecting a given matching video fromthe plurality of candidate matching videos based on a rank representingsimilarity of the given matching video to the video received by theapplication server; and accessing an augmented reality experiencecorresponding to the given matching video.
 20. A non-transitorymachine-readable storage medium that includes instructions that, whenexecuted by one or more processors of a machine, cause the machine toperform operations comprising: identifying a plurality of features forframes of a video received by an application server; assigning a firstsequence of a first subset of the plurality of features and a secondsequence of a second subset of the plurality of features respectively toa first visual codebook cluster and a second visual codebook cluster;applying the first and second visual codebook clusters to a visualsearch database to identify a plurality of candidate matching videos,the plurality of candidate matching videos being identified in responseto computing cosine similarity between term frequency informationassociated with the video and term frequency information associated witha plurality of marker videos, the plurality of candidate matching videosbeing a first subset of the plurality of marker videos having a greatercosine similarity than a second subset of the plurality of markervideos; selecting a given matching video from the plurality of candidatematching videos based on a rank representing similarity of the givenmatching video to the video received by the application server; andaccessing an augmented reality experience corresponding to the givenmatching video.